id int32 0 252k | repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
value | code stringlengths 75 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 |
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15,800 | mitodl/edx-api-client | edx_api/enrollments/__init__.py | CourseEnrollments._get_enrollments_list_page | def _get_enrollments_list_page(self, params=None):
"""
Submit request to retrieve enrollments list.
Args:
params (dict): Query parameters to use in the request. Valid parameters are:
* course_id: Filters the result to course enrollments for the course
... | python | def _get_enrollments_list_page(self, params=None):
"""
Submit request to retrieve enrollments list.
Args:
params (dict): Query parameters to use in the request. Valid parameters are:
* course_id: Filters the result to course enrollments for the course
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15,801 | mitodl/edx-api-client | edx_api/enrollments/__init__.py | CourseEnrollments.get_enrollments | def get_enrollments(self, course_id=None, usernames=None):
"""
List all course enrollments.
Args:
course_id (str, optional): If used enrollments will be filtered to the specified
course id.
usernames (list, optional): List of usernames to filter enrollmen... | python | def get_enrollments(self, course_id=None, usernames=None):
"""
List all course enrollments.
Args:
course_id (str, optional): If used enrollments will be filtered to the specified
course id.
usernames (list, optional): List of usernames to filter enrollmen... | [
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15,802 | mitodl/edx-api-client | edx_api/enrollments/__init__.py | CourseEnrollments.get_student_enrollments | def get_student_enrollments(self):
"""
Returns an Enrollments object with the user enrollments
Returns:
Enrollments: object representing the student enrollments
"""
# the request is done in behalf of the current logged in user
resp = self.requester.get(
... | python | def get_student_enrollments(self):
"""
Returns an Enrollments object with the user enrollments
Returns:
Enrollments: object representing the student enrollments
"""
# the request is done in behalf of the current logged in user
resp = self.requester.get(
... | [
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15,803 | mitodl/edx-api-client | edx_api/enrollments/__init__.py | CourseEnrollments.create_audit_student_enrollment | def create_audit_student_enrollment(self, course_id):
"""
Creates an audit enrollment for the user in a given course
Args:
course_id (str): an edX course id
Returns:
Enrollment: object representing the student enrollment in the provided course
"""
... | python | def create_audit_student_enrollment(self, course_id):
"""
Creates an audit enrollment for the user in a given course
Args:
course_id (str): an edX course id
Returns:
Enrollment: object representing the student enrollment in the provided course
"""
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15,804 | mitodl/edx-api-client | edx_api/certificates/__init__.py | UserCertificates.get_student_certificate | def get_student_certificate(self, username, course_id):
"""
Returns an Certificate object with the user certificates
Args:
username (str): an edx user's username
course_id (str): an edX course id.
Returns:
Certificate: object representing the student... | python | def get_student_certificate(self, username, course_id):
"""
Returns an Certificate object with the user certificates
Args:
username (str): an edx user's username
course_id (str): an edX course id.
Returns:
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15,805 | mitodl/edx-api-client | edx_api/certificates/__init__.py | UserCertificates.get_student_certificates | def get_student_certificates(self, username, course_ids=None):
"""
Returns an Certificates object with the user certificates
Args:
username (str): an edx user's username
course_ids (list): a list of edX course ids.
Returns:
Certificates: object repre... | python | def get_student_certificates(self, username, course_ids=None):
"""
Returns an Certificates object with the user certificates
Args:
username (str): an edx user's username
course_ids (list): a list of edX course ids.
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Certificates: object repre... | [
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15,806 | metakirby5/colorz | colorz.py | get_colors | def get_colors(img):
"""
Returns a list of all the image's colors.
"""
w, h = img.size
return [color[:3] for count, color in img.convert('RGB').getcolors(w * h)] | python | def get_colors(img):
"""
Returns a list of all the image's colors.
"""
w, h = img.size
return [color[:3] for count, color in img.convert('RGB').getcolors(w * h)] | [
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15,807 | metakirby5/colorz | colorz.py | clamp | def clamp(color, min_v, max_v):
"""
Clamps a color such that the value is between min_v and max_v.
"""
h, s, v = rgb_to_hsv(*map(down_scale, color))
min_v, max_v = map(down_scale, (min_v, max_v))
v = min(max(min_v, v), max_v)
return tuple(map(up_scale, hsv_to_rgb(h, s, v))) | python | def clamp(color, min_v, max_v):
"""
Clamps a color such that the value is between min_v and max_v.
"""
h, s, v = rgb_to_hsv(*map(down_scale, color))
min_v, max_v = map(down_scale, (min_v, max_v))
v = min(max(min_v, v), max_v)
return tuple(map(up_scale, hsv_to_rgb(h, s, v))) | [
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15,808 | metakirby5/colorz | colorz.py | order_by_hue | def order_by_hue(colors):
"""
Orders colors by hue.
"""
hsvs = [rgb_to_hsv(*map(down_scale, color)) for color in colors]
hsvs.sort(key=lambda t: t[0])
return [tuple(map(up_scale, hsv_to_rgb(*hsv))) for hsv in hsvs] | python | def order_by_hue(colors):
"""
Orders colors by hue.
"""
hsvs = [rgb_to_hsv(*map(down_scale, color)) for color in colors]
hsvs.sort(key=lambda t: t[0])
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15,809 | metakirby5/colorz | colorz.py | brighten | def brighten(color, brightness):
"""
Adds or subtracts value to a color.
"""
h, s, v = rgb_to_hsv(*map(down_scale, color))
return tuple(map(up_scale, hsv_to_rgb(h, s, v + down_scale(brightness)))) | python | def brighten(color, brightness):
"""
Adds or subtracts value to a color.
"""
h, s, v = rgb_to_hsv(*map(down_scale, color))
return tuple(map(up_scale, hsv_to_rgb(h, s, v + down_scale(brightness)))) | [
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15,810 | metakirby5/colorz | colorz.py | colorz | def colorz(fd, n=DEFAULT_NUM_COLORS, min_v=DEFAULT_MINV, max_v=DEFAULT_MAXV,
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"""
Get the n most dominant colors of an image.
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"""
Get the n most dominant colors of an image.
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15,811 | ansible/pytest-ansible | setup.py | long_description | def long_description(*paths):
'''Returns a RST formated string.
'''
result = ''
# attempt to import pandoc
try:
import pypandoc
except (ImportError, OSError) as e:
print("Unable to import pypandoc - %s" % e)
return result
# attempt md -> rst conversion
try:
... | python | def long_description(*paths):
'''Returns a RST formated string.
'''
result = ''
# attempt to import pandoc
try:
import pypandoc
except (ImportError, OSError) as e:
print("Unable to import pypandoc - %s" % e)
return result
# attempt md -> rst conversion
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15,812 | wmayner/pyphi | pyphi/cache.py | memory_full | def memory_full():
"""Check if the memory is too full for further caching."""
current_process = psutil.Process(os.getpid())
return (current_process.memory_percent() >
config.MAXIMUM_CACHE_MEMORY_PERCENTAGE) | python | def memory_full():
"""Check if the memory is too full for further caching."""
current_process = psutil.Process(os.getpid())
return (current_process.memory_percent() >
config.MAXIMUM_CACHE_MEMORY_PERCENTAGE) | [
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15,813 | wmayner/pyphi | pyphi/cache.py | cache | def cache(cache={}, maxmem=config.MAXIMUM_CACHE_MEMORY_PERCENTAGE,
typed=False):
"""Memory-limited cache decorator.
``maxmem`` is a float between 0 and 100, inclusive, specifying the maximum
percentage of physical memory that the cache can use.
If ``typed`` is ``True``, arguments of differen... | python | def cache(cache={}, maxmem=config.MAXIMUM_CACHE_MEMORY_PERCENTAGE,
typed=False):
"""Memory-limited cache decorator.
``maxmem`` is a float between 0 and 100, inclusive, specifying the maximum
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15,814 | wmayner/pyphi | pyphi/cache.py | MICECache | def MICECache(subsystem, parent_cache=None):
"""Construct a |MICE| cache.
Uses either a Redis-backed cache or a local dict cache on the object.
Args:
subsystem (Subsystem): The subsystem that this is a cache for.
Kwargs:
parent_cache (MICECache): The cache generated by the uncut
... | python | def MICECache(subsystem, parent_cache=None):
"""Construct a |MICE| cache.
Uses either a Redis-backed cache or a local dict cache on the object.
Args:
subsystem (Subsystem): The subsystem that this is a cache for.
Kwargs:
parent_cache (MICECache): The cache generated by the uncut
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15,815 | wmayner/pyphi | pyphi/cache.py | method | def method(cache_name, key_prefix=None):
"""Caching decorator for object-level method caches.
Cache key generation is delegated to the cache.
Args:
cache_name (str): The name of the (already-instantiated) cache
on the decorated object which should be used to store results
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"""Caching decorator for object-level method caches.
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cache_name (str): The name of the (already-instantiated) cache
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15,816 | wmayner/pyphi | pyphi/cache.py | DictCache.get | def get(self, key):
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Returns None if the key is not in the cache. Updates cache
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if key in self.cache:
self.hits += 1
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"""Get a value out of the cache.
Returns None if the key is not in the cache. Updates cache
statistics.
"""
if key in self.cache:
self.hits += 1
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15,817 | wmayner/pyphi | pyphi/cache.py | DictCache.key | def key(self, *args, _prefix=None, **kwargs):
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15,818 | wmayner/pyphi | pyphi/cache.py | RedisCache.info | def info(self):
"""Return cache information.
.. note:: This is not the cache info for the entire Redis key space.
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.. note:: This is not the cache info for the entire Redis key space.
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15,819 | wmayner/pyphi | pyphi/cache.py | RedisMICECache.set | def set(self, key, value):
"""Only need to set if the subsystem is uncut.
Caches are only inherited from uncut subsystems.
"""
if not self.subsystem.is_cut:
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"""Only need to set if the subsystem is uncut.
Caches are only inherited from uncut subsystems.
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15,820 | wmayner/pyphi | pyphi/cache.py | DictMICECache._build | def _build(self, parent_cache):
"""Build the initial cache from the parent.
Only include the |MICE| which are unaffected by the subsystem cut.
A |MICE| is affected if either the cut splits the mechanism
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"""Build the initial cache from the parent.
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15,821 | wmayner/pyphi | pyphi/cache.py | PurviewCache.set | def set(self, key, value):
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self.cache[key] = value | python | def set(self, key, value):
"""Only set if purview caching is enabled"""
if config.CACHE_POTENTIAL_PURVIEWS:
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15,822 | wmayner/pyphi | pyphi/connectivity.py | apply_boundary_conditions_to_cm | def apply_boundary_conditions_to_cm(external_indices, cm):
"""Remove connections to or from external nodes."""
cm = cm.copy()
cm[external_indices, :] = 0 # Zero-out row
cm[:, external_indices] = 0 # Zero-out columnt
return cm | python | def apply_boundary_conditions_to_cm(external_indices, cm):
"""Remove connections to or from external nodes."""
cm = cm.copy()
cm[external_indices, :] = 0 # Zero-out row
cm[:, external_indices] = 0 # Zero-out columnt
return cm | [
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15,823 | wmayner/pyphi | pyphi/connectivity.py | get_inputs_from_cm | def get_inputs_from_cm(index, cm):
"""Return indices of inputs to the node with the given index."""
return tuple(i for i in range(cm.shape[0]) if cm[i][index]) | python | def get_inputs_from_cm(index, cm):
"""Return indices of inputs to the node with the given index."""
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15,824 | wmayner/pyphi | pyphi/connectivity.py | get_outputs_from_cm | def get_outputs_from_cm(index, cm):
"""Return indices of the outputs of node with the given index."""
return tuple(i for i in range(cm.shape[0]) if cm[index][i]) | python | def get_outputs_from_cm(index, cm):
"""Return indices of the outputs of node with the given index."""
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15,825 | wmayner/pyphi | pyphi/connectivity.py | causally_significant_nodes | def causally_significant_nodes(cm):
"""Return indices of nodes that have both inputs and outputs."""
inputs = cm.sum(0)
outputs = cm.sum(1)
nodes_with_inputs_and_outputs = np.logical_and(inputs > 0, outputs > 0)
return tuple(np.where(nodes_with_inputs_and_outputs)[0]) | python | def causally_significant_nodes(cm):
"""Return indices of nodes that have both inputs and outputs."""
inputs = cm.sum(0)
outputs = cm.sum(1)
nodes_with_inputs_and_outputs = np.logical_and(inputs > 0, outputs > 0)
return tuple(np.where(nodes_with_inputs_and_outputs)[0]) | [
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15,826 | wmayner/pyphi | pyphi/connectivity.py | relevant_connections | def relevant_connections(n, _from, to):
"""Construct a connectivity matrix.
Args:
n (int): The dimensions of the matrix
_from (tuple[int]): Nodes with outgoing connections to ``to``
to (tuple[int]): Nodes with incoming connections from ``_from``
Returns:
np.ndarray: An |n x... | python | def relevant_connections(n, _from, to):
"""Construct a connectivity matrix.
Args:
n (int): The dimensions of the matrix
_from (tuple[int]): Nodes with outgoing connections to ``to``
to (tuple[int]): Nodes with incoming connections from ``_from``
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np.ndarray: An |n x... | [
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15,827 | wmayner/pyphi | pyphi/connectivity.py | block_cm | def block_cm(cm):
"""Return whether ``cm`` can be arranged as a block connectivity matrix.
If so, the corresponding mechanism/purview is trivially reducible.
Technically, only square matrices are "block diagonal", but the notion of
connectivity carries over.
We test for block connectivity by tryin... | python | def block_cm(cm):
"""Return whether ``cm`` can be arranged as a block connectivity matrix.
If so, the corresponding mechanism/purview is trivially reducible.
Technically, only square matrices are "block diagonal", but the notion of
connectivity carries over.
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15,828 | wmayner/pyphi | pyphi/connectivity.py | block_reducible | def block_reducible(cm, nodes1, nodes2):
"""Return whether connections from ``nodes1`` to ``nodes2`` are reducible.
Args:
cm (np.ndarray): The network's connectivity matrix.
nodes1 (tuple[int]): Source nodes
nodes2 (tuple[int]): Sink nodes
"""
# Trivial case
if not nodes1 or... | python | def block_reducible(cm, nodes1, nodes2):
"""Return whether connections from ``nodes1`` to ``nodes2`` are reducible.
Args:
cm (np.ndarray): The network's connectivity matrix.
nodes1 (tuple[int]): Source nodes
nodes2 (tuple[int]): Sink nodes
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15,829 | wmayner/pyphi | pyphi/connectivity.py | _connected | def _connected(cm, nodes, connection):
"""Test connectivity for the connectivity matrix."""
if nodes is not None:
cm = cm[np.ix_(nodes, nodes)]
num_components, _ = connected_components(cm, connection=connection)
return num_components < 2 | python | def _connected(cm, nodes, connection):
"""Test connectivity for the connectivity matrix."""
if nodes is not None:
cm = cm[np.ix_(nodes, nodes)]
num_components, _ = connected_components(cm, connection=connection)
return num_components < 2 | [
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15,830 | wmayner/pyphi | pyphi/connectivity.py | is_full | def is_full(cm, nodes1, nodes2):
"""Test connectivity of one set of nodes to another.
Args:
cm (``np.ndarrray``): The connectivity matrix
nodes1 (tuple[int]): The nodes whose outputs to ``nodes2`` will be
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Args:
cm (``np.ndarrray``): The connectivity matrix
nodes1 (tuple[int]): The nodes whose outputs to ``nodes2`` will be
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15,831 | wmayner/pyphi | pyphi/models/cuts.py | _CutBase.apply_cut | def apply_cut(self, cm):
"""Return a modified connectivity matrix with all connections that are
severed by this cut removed.
Args:
cm (np.ndarray): A connectivity matrix.
"""
# Invert the cut matrix, creating a matrix of preserved connections
inverse = np.log... | python | def apply_cut(self, cm):
"""Return a modified connectivity matrix with all connections that are
severed by this cut removed.
Args:
cm (np.ndarray): A connectivity matrix.
"""
# Invert the cut matrix, creating a matrix of preserved connections
inverse = np.log... | [
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15,832 | wmayner/pyphi | pyphi/models/cuts.py | _CutBase.cuts_connections | def cuts_connections(self, a, b):
"""Check if this cut severs any connections from ``a`` to ``b``.
Args:
a (tuple[int]): A set of nodes.
b (tuple[int]): A set of nodes.
"""
n = max(self.indices) + 1
return self.cut_matrix(n)[np.ix_(a, b)].any() | python | def cuts_connections(self, a, b):
"""Check if this cut severs any connections from ``a`` to ``b``.
Args:
a (tuple[int]): A set of nodes.
b (tuple[int]): A set of nodes.
"""
n = max(self.indices) + 1
return self.cut_matrix(n)[np.ix_(a, b)].any() | [
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15,833 | wmayner/pyphi | pyphi/models/cuts.py | _CutBase.all_cut_mechanisms | def all_cut_mechanisms(self):
"""Return all mechanisms with elements on both sides of this cut.
Yields:
tuple[int]: The next cut mechanism.
"""
for mechanism in utils.powerset(self.indices, nonempty=True):
if self.splits_mechanism(mechanism):
yiel... | python | def all_cut_mechanisms(self):
"""Return all mechanisms with elements on both sides of this cut.
Yields:
tuple[int]: The next cut mechanism.
"""
for mechanism in utils.powerset(self.indices, nonempty=True):
if self.splits_mechanism(mechanism):
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15,834 | wmayner/pyphi | pyphi/models/cuts.py | Cut.cut_matrix | def cut_matrix(self, n):
"""Compute the cut matrix for this cut.
The cut matrix is a square matrix which represents connections severed
by the cut.
Args:
n (int): The size of the network.
Example:
>>> cut = Cut((1,), (2,))
>>> cut.cut_matrix(... | python | def cut_matrix(self, n):
"""Compute the cut matrix for this cut.
The cut matrix is a square matrix which represents connections severed
by the cut.
Args:
n (int): The size of the network.
Example:
>>> cut = Cut((1,), (2,))
>>> cut.cut_matrix(... | [
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15,835 | wmayner/pyphi | pyphi/models/cuts.py | KCut.cut_matrix | def cut_matrix(self, n):
"""The matrix of connections that are severed by this cut."""
cm = np.zeros((n, n))
for part in self.partition:
from_, to = self.direction.order(part.mechanism, part.purview)
# All indices external to this part
external = tuple(set(se... | python | def cut_matrix(self, n):
"""The matrix of connections that are severed by this cut."""
cm = np.zeros((n, n))
for part in self.partition:
from_, to = self.direction.order(part.mechanism, part.purview)
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15,836 | wmayner/pyphi | pyphi/compute/distance.py | concept_distance | def concept_distance(c1, c2):
"""Return the distance between two concepts in concept space.
Args:
c1 (Concept): The first concept.
c2 (Concept): The second concept.
Returns:
float: The distance between the two concepts in concept space.
"""
# Calculate the sum of the cause ... | python | def concept_distance(c1, c2):
"""Return the distance between two concepts in concept space.
Args:
c1 (Concept): The first concept.
c2 (Concept): The second concept.
Returns:
float: The distance between the two concepts in concept space.
"""
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15,837 | wmayner/pyphi | pyphi/compute/distance.py | small_phi_ces_distance | def small_phi_ces_distance(C1, C2):
"""Return the difference in |small_phi| between |CauseEffectStructure|."""
return sum(c.phi for c in C1) - sum(c.phi for c in C2) | python | def small_phi_ces_distance(C1, C2):
"""Return the difference in |small_phi| between |CauseEffectStructure|."""
return sum(c.phi for c in C1) - sum(c.phi for c in C2) | [
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15,838 | wmayner/pyphi | pyphi/node.py | generate_nodes | def generate_nodes(tpm, cm, network_state, indices, node_labels=None):
"""Generate |Node| objects for a subsystem.
Args:
tpm (np.ndarray): The system's TPM
cm (np.ndarray): The corresponding CM.
network_state (tuple): The state of the network.
indices (tuple[int]): Indices to ge... | python | def generate_nodes(tpm, cm, network_state, indices, node_labels=None):
"""Generate |Node| objects for a subsystem.
Args:
tpm (np.ndarray): The system's TPM
cm (np.ndarray): The corresponding CM.
network_state (tuple): The state of the network.
indices (tuple[int]): Indices to ge... | [
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15,839 | wmayner/pyphi | pyphi/node.py | expand_node_tpm | def expand_node_tpm(tpm):
"""Broadcast a node TPM over the full network.
This is different from broadcasting the TPM of a full system since the last
dimension (containing the state of the node) contains only the probability
of *this* node being on, rather than the probabilities for each node.
"""
... | python | def expand_node_tpm(tpm):
"""Broadcast a node TPM over the full network.
This is different from broadcasting the TPM of a full system since the last
dimension (containing the state of the node) contains only the probability
of *this* node being on, rather than the probabilities for each node.
"""
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15,840 | wmayner/pyphi | pyphi/tpm.py | condition_tpm | def condition_tpm(tpm, fixed_nodes, state):
"""Return a TPM conditioned on the given fixed node indices, whose states
are fixed according to the given state-tuple.
The dimensions of the new TPM that correspond to the fixed nodes are
collapsed onto their state, making those dimensions singletons suitabl... | python | def condition_tpm(tpm, fixed_nodes, state):
"""Return a TPM conditioned on the given fixed node indices, whose states
are fixed according to the given state-tuple.
The dimensions of the new TPM that correspond to the fixed nodes are
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15,841 | wmayner/pyphi | pyphi/tpm.py | expand_tpm | def expand_tpm(tpm):
"""Broadcast a state-by-node TPM so that singleton dimensions are expanded
over the full network.
"""
unconstrained = np.ones([2] * (tpm.ndim - 1) + [tpm.shape[-1]])
return tpm * unconstrained | python | def expand_tpm(tpm):
"""Broadcast a state-by-node TPM so that singleton dimensions are expanded
over the full network.
"""
unconstrained = np.ones([2] * (tpm.ndim - 1) + [tpm.shape[-1]])
return tpm * unconstrained | [
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15,842 | wmayner/pyphi | pyphi/tpm.py | marginalize_out | def marginalize_out(node_indices, tpm):
"""Marginalize out nodes from a TPM.
Args:
node_indices (list[int]): The indices of nodes to be marginalized out.
tpm (np.ndarray): The TPM to marginalize the node out of.
Returns:
np.ndarray: A TPM with the same number of dimensions, with th... | python | def marginalize_out(node_indices, tpm):
"""Marginalize out nodes from a TPM.
Args:
node_indices (list[int]): The indices of nodes to be marginalized out.
tpm (np.ndarray): The TPM to marginalize the node out of.
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15,843 | wmayner/pyphi | pyphi/tpm.py | infer_edge | def infer_edge(tpm, a, b, contexts):
"""Infer the presence or absence of an edge from node A to node B.
Let |S| be the set of all nodes in a network. Let |A' = S - {A}|. We call
the state of |A'| the context |C| of |A|. There is an edge from |A| to |B|
if there exists any context |C(A)| such that |Pr(B... | python | def infer_edge(tpm, a, b, contexts):
"""Infer the presence or absence of an edge from node A to node B.
Let |S| be the set of all nodes in a network. Let |A' = S - {A}|. We call
the state of |A'| the context |C| of |A|. There is an edge from |A| to |B|
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15,844 | wmayner/pyphi | pyphi/tpm.py | infer_cm | def infer_cm(tpm):
"""Infer the connectivity matrix associated with a state-by-node TPM in
multidimensional form.
"""
network_size = tpm.shape[-1]
all_contexts = tuple(all_states(network_size - 1))
cm = np.empty((network_size, network_size), dtype=int)
for a, b in np.ndindex(cm.shape):
... | python | def infer_cm(tpm):
"""Infer the connectivity matrix associated with a state-by-node TPM in
multidimensional form.
"""
network_size = tpm.shape[-1]
all_contexts = tuple(all_states(network_size - 1))
cm = np.empty((network_size, network_size), dtype=int)
for a, b in np.ndindex(cm.shape):
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15,845 | wmayner/pyphi | pyphi/compute/parallel.py | get_num_processes | def get_num_processes():
"""Return the number of processes to use in parallel."""
cpu_count = multiprocessing.cpu_count()
if config.NUMBER_OF_CORES == 0:
raise ValueError(
'Invalid NUMBER_OF_CORES; value may not be 0.')
if config.NUMBER_OF_CORES > cpu_count:
log.info('Reque... | python | def get_num_processes():
"""Return the number of processes to use in parallel."""
cpu_count = multiprocessing.cpu_count()
if config.NUMBER_OF_CORES == 0:
raise ValueError(
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15,846 | wmayner/pyphi | pyphi/compute/parallel.py | MapReduce.init_progress_bar | def init_progress_bar(self):
"""Initialize and return a progress bar."""
# Forked worker processes can't show progress bars.
disable = MapReduce._forked or not config.PROGRESS_BARS
# Don't materialize iterable unless we have to: huge iterables
# (e.g. of `KCuts`) eat memory.
... | python | def init_progress_bar(self):
"""Initialize and return a progress bar."""
# Forked worker processes can't show progress bars.
disable = MapReduce._forked or not config.PROGRESS_BARS
# Don't materialize iterable unless we have to: huge iterables
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15,847 | wmayner/pyphi | pyphi/compute/parallel.py | MapReduce.worker | def worker(compute, task_queue, result_queue, log_queue, complete,
*context):
"""A worker process, run by ``multiprocessing.Process``."""
try:
MapReduce._forked = True
log.debug('Worker process starting...')
configure_worker_logging(log_queue)
... | python | def worker(compute, task_queue, result_queue, log_queue, complete,
*context):
"""A worker process, run by ``multiprocessing.Process``."""
try:
MapReduce._forked = True
log.debug('Worker process starting...')
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15,848 | wmayner/pyphi | pyphi/compute/parallel.py | MapReduce.start_parallel | def start_parallel(self):
"""Initialize all queues and start the worker processes and the log
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"""
self.num_processes = get_num_processes()
self.task_queue = multiprocessing.Queue(maxsize=Q_MAX_SIZE)
self.result_queue = multiprocessing.Queue()
self.log_que... | python | def start_parallel(self):
"""Initialize all queues and start the worker processes and the log
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"""
self.num_processes = get_num_processes()
self.task_queue = multiprocessing.Queue(maxsize=Q_MAX_SIZE)
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15,849 | wmayner/pyphi | pyphi/compute/parallel.py | MapReduce.initialize_tasks | def initialize_tasks(self):
"""Load the input queue to capacity.
Overfilling causes a deadlock when `queue.put` blocks when
full, so further tasks are enqueued as results are returned.
"""
# Add a poison pill to shutdown each process.
self.tasks = chain(self.iterable, [P... | python | def initialize_tasks(self):
"""Load the input queue to capacity.
Overfilling causes a deadlock when `queue.put` blocks when
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"""
# Add a poison pill to shutdown each process.
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15,850 | wmayner/pyphi | pyphi/compute/parallel.py | MapReduce.maybe_put_task | def maybe_put_task(self):
"""Enqueue the next task, if there are any waiting."""
try:
task = next(self.tasks)
except StopIteration:
pass
else:
log.debug('Putting %s on queue', task)
self.task_queue.put(task) | python | def maybe_put_task(self):
"""Enqueue the next task, if there are any waiting."""
try:
task = next(self.tasks)
except StopIteration:
pass
else:
log.debug('Putting %s on queue', task)
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15,851 | wmayner/pyphi | pyphi/compute/parallel.py | MapReduce.run_parallel | def run_parallel(self):
"""Perform the computation in parallel, reading results from the output
queue and passing them to ``process_result``.
"""
try:
self.start_parallel()
result = self.empty_result(*self.context)
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try:
self.start_parallel()
result = self.empty_result(*self.context)
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15,852 | wmayner/pyphi | pyphi/compute/parallel.py | MapReduce.finish_parallel | def finish_parallel(self):
"""Orderly shutdown of workers."""
for process in self.processes:
process.join()
# Shutdown the log thread
log.debug('Joining log thread')
self.log_queue.put(POISON_PILL)
self.log_thread.join()
self.log_queue.close()
... | python | def finish_parallel(self):
"""Orderly shutdown of workers."""
for process in self.processes:
process.join()
# Shutdown the log thread
log.debug('Joining log thread')
self.log_queue.put(POISON_PILL)
self.log_thread.join()
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15,853 | wmayner/pyphi | pyphi/compute/parallel.py | MapReduce.run_sequential | def run_sequential(self):
"""Perform the computation sequentially, only holding two computed
objects in memory at a time.
"""
try:
result = self.empty_result(*self.context)
for obj in self.iterable:
r = self.compute(obj, *self.context)
... | python | def run_sequential(self):
"""Perform the computation sequentially, only holding two computed
objects in memory at a time.
"""
try:
result = self.empty_result(*self.context)
for obj in self.iterable:
r = self.compute(obj, *self.context)
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15,854 | wmayner/pyphi | pyphi/conf.py | configure_logging | def configure_logging(conf):
"""Reconfigure PyPhi logging based on the current configuration."""
logging.config.dictConfig({
'version': 1,
'disable_existing_loggers': False,
'formatters': {
'standard': {
'format': '%(asctime)s [%(name)s] %(levelname)s '
... | python | def configure_logging(conf):
"""Reconfigure PyPhi logging based on the current configuration."""
logging.config.dictConfig({
'version': 1,
'disable_existing_loggers': False,
'formatters': {
'standard': {
'format': '%(asctime)s [%(name)s] %(levelname)s '
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15,855 | wmayner/pyphi | pyphi/conf.py | Option._validate | def _validate(self, value):
"""Validate the new value."""
if self.values and value not in self.values:
raise ValueError(
'{} is not a valid value for {}'.format(value, self.name)) | python | def _validate(self, value):
"""Validate the new value."""
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15,856 | wmayner/pyphi | pyphi/conf.py | Config.options | def options(cls):
"""Return a dictionary of the ``Option`` objects for this config."""
return {k: v for k, v in cls.__dict__.items() if isinstance(v, Option)} | python | def options(cls):
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15,857 | wmayner/pyphi | pyphi/conf.py | Config.defaults | def defaults(self):
"""Return the default values of this configuration."""
return {k: v.default for k, v in self.options().items()} | python | def defaults(self):
"""Return the default values of this configuration."""
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15,858 | wmayner/pyphi | pyphi/conf.py | Config.load_dict | def load_dict(self, dct):
"""Load a dictionary of configuration values."""
for k, v in dct.items():
setattr(self, k, v) | python | def load_dict(self, dct):
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15,859 | wmayner/pyphi | pyphi/conf.py | Config.load_file | def load_file(self, filename):
"""Load config from a YAML file."""
filename = os.path.abspath(filename)
with open(filename) as f:
self.load_dict(yaml.load(f))
self._loaded_files.append(filename) | python | def load_file(self, filename):
"""Load config from a YAML file."""
filename = os.path.abspath(filename)
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15,860 | wmayner/pyphi | pyphi/conf.py | PyphiConfig.log | def log(self):
"""Log current settings."""
log.info('PyPhi v%s', __about__.__version__)
if self._loaded_files:
log.info('Loaded configuration from %s', self._loaded_files)
else:
log.info('Using default configuration (no configuration file '
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"""Log current settings."""
log.info('PyPhi v%s', __about__.__version__)
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log.info('Loaded configuration from %s', self._loaded_files)
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log.info('Using default configuration (no configuration file '
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15,861 | wmayner/pyphi | pyphi/convert.py | be2le_state_by_state | def be2le_state_by_state(tpm):
"""Convert a state-by-state TPM from big-endian to little-endian or vice
versa.
Args:
tpm (np.ndarray): A state-by-state TPM.
Returns:
np.ndarray: The state-by-state TPM in the other indexing format.
Example:
>>> tpm = np.arange(16).reshape([... | python | def be2le_state_by_state(tpm):
"""Convert a state-by-state TPM from big-endian to little-endian or vice
versa.
Args:
tpm (np.ndarray): A state-by-state TPM.
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np.ndarray: The state-by-state TPM in the other indexing format.
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15,862 | wmayner/pyphi | pyphi/convert.py | to_multidimensional | def to_multidimensional(tpm):
"""Reshape a state-by-node TPM to the multidimensional form.
See documentation for the |Network| object for more information on TPM
formats.
"""
# Cast to np.array.
tpm = np.array(tpm)
# Get the number of nodes.
N = tpm.shape[-1]
# Reshape. We use Fortr... | python | def to_multidimensional(tpm):
"""Reshape a state-by-node TPM to the multidimensional form.
See documentation for the |Network| object for more information on TPM
formats.
"""
# Cast to np.array.
tpm = np.array(tpm)
# Get the number of nodes.
N = tpm.shape[-1]
# Reshape. We use Fortr... | [
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15,863 | wmayner/pyphi | pyphi/convert.py | state_by_state2state_by_node | def state_by_state2state_by_node(tpm):
"""Convert a state-by-state TPM to a state-by-node TPM.
.. danger::
Many nondeterministic state-by-state TPMs can be represented by a
single a state-by-state TPM. However, the mapping can be made to be
one-to-one if we assume the state-by-state TPM... | python | def state_by_state2state_by_node(tpm):
"""Convert a state-by-state TPM to a state-by-node TPM.
.. danger::
Many nondeterministic state-by-state TPMs can be represented by a
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15,864 | wmayner/pyphi | pyphi/convert.py | state_by_node2state_by_state | def state_by_node2state_by_state(tpm):
"""Convert a state-by-node TPM to a state-by-state TPM.
.. important::
A nondeterministic state-by-node TPM can have more than one
representation as a state-by-state TPM. However, the mapping can be
made to be one-to-one if we assume the TPMs to be... | python | def state_by_node2state_by_state(tpm):
"""Convert a state-by-node TPM to a state-by-state TPM.
.. important::
A nondeterministic state-by-node TPM can have more than one
representation as a state-by-state TPM. However, the mapping can be
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15,865 | wmayner/pyphi | profiling/code_to_profile.py | load_json_network | def load_json_network(json_dict):
"""Load a network from a json file"""
network = pyphi.Network.from_json(json_dict['network'])
state = json_dict['state']
return (network, state) | python | def load_json_network(json_dict):
"""Load a network from a json file"""
network = pyphi.Network.from_json(json_dict['network'])
state = json_dict['state']
return (network, state) | [
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15,866 | wmayner/pyphi | profiling/code_to_profile.py | all_network_files | def all_network_files():
"""All network files"""
# TODO: list explicitly since some are missing?
network_types = [
'AND-circle',
'MAJ-specialized',
'MAJ-complete',
'iit-3.0-modular'
]
network_sizes = range(5, 8)
network_files = []
for n in network_sizes:
... | python | def all_network_files():
"""All network files"""
# TODO: list explicitly since some are missing?
network_types = [
'AND-circle',
'MAJ-specialized',
'MAJ-complete',
'iit-3.0-modular'
]
network_sizes = range(5, 8)
network_files = []
for n in network_sizes:
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15,867 | wmayner/pyphi | profiling/code_to_profile.py | profile_network | def profile_network(filename):
"""Profile a network.
Saves PyPhi results, pstats, and logs to respective directories.
"""
log = logging.getLogger(filename)
logfile = os.path.join(LOGS, filename + '.log')
os.makedirs(os.path.dirname(logfile), exist_ok=True)
handler = logging.FileHandler(logf... | python | def profile_network(filename):
"""Profile a network.
Saves PyPhi results, pstats, and logs to respective directories.
"""
log = logging.getLogger(filename)
logfile = os.path.join(LOGS, filename + '.log')
os.makedirs(os.path.dirname(logfile), exist_ok=True)
handler = logging.FileHandler(logf... | [
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15,868 | wmayner/pyphi | pyphi/timescale.py | run_tpm | def run_tpm(tpm, time_scale):
"""Iterate a TPM by the specified number of time steps.
Args:
tpm (np.ndarray): A state-by-node tpm.
time_scale (int): The number of steps to run the tpm.
Returns:
np.ndarray
"""
sbs_tpm = convert.state_by_node2state_by_state(tpm)
if sparse... | python | def run_tpm(tpm, time_scale):
"""Iterate a TPM by the specified number of time steps.
Args:
tpm (np.ndarray): A state-by-node tpm.
time_scale (int): The number of steps to run the tpm.
Returns:
np.ndarray
"""
sbs_tpm = convert.state_by_node2state_by_state(tpm)
if sparse... | [
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15,869 | wmayner/pyphi | pyphi/timescale.py | run_cm | def run_cm(cm, time_scale):
"""Iterate a connectivity matrix the specified number of steps.
Args:
cm (np.ndarray): A connectivity matrix.
time_scale (int): The number of steps to run.
Returns:
np.ndarray: The connectivity matrix at the new timescale.
"""
cm = np.linalg.matr... | python | def run_cm(cm, time_scale):
"""Iterate a connectivity matrix the specified number of steps.
Args:
cm (np.ndarray): A connectivity matrix.
time_scale (int): The number of steps to run.
Returns:
np.ndarray: The connectivity matrix at the new timescale.
"""
cm = np.linalg.matr... | [
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15,870 | wmayner/pyphi | pyphi/compute/network.py | _reachable_subsystems | def _reachable_subsystems(network, indices, state):
"""A generator over all subsystems in a valid state."""
validate.is_network(network)
# Return subsystems largest to smallest to optimize parallel
# resource usage.
for subset in utils.powerset(indices, nonempty=True, reverse=True):
try:
... | python | def _reachable_subsystems(network, indices, state):
"""A generator over all subsystems in a valid state."""
validate.is_network(network)
# Return subsystems largest to smallest to optimize parallel
# resource usage.
for subset in utils.powerset(indices, nonempty=True, reverse=True):
try:
... | [
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15,871 | wmayner/pyphi | pyphi/compute/network.py | all_complexes | def all_complexes(network, state):
"""Return a generator for all complexes of the network.
.. note::
Includes reducible, zero-|big_phi| complexes (which are not, strictly
speaking, complexes at all).
Args:
network (Network): The |Network| of interest.
state (tuple[int]): Th... | python | def all_complexes(network, state):
"""Return a generator for all complexes of the network.
.. note::
Includes reducible, zero-|big_phi| complexes (which are not, strictly
speaking, complexes at all).
Args:
network (Network): The |Network| of interest.
state (tuple[int]): Th... | [
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15,872 | wmayner/pyphi | pyphi/compute/network.py | complexes | def complexes(network, state):
"""Return all irreducible complexes of the network.
Args:
network (Network): The |Network| of interest.
state (tuple[int]): The state of the network (a binary tuple).
Yields:
SystemIrreducibilityAnalysis: A |SIA| for each |Subsystem| of the
|N... | python | def complexes(network, state):
"""Return all irreducible complexes of the network.
Args:
network (Network): The |Network| of interest.
state (tuple[int]): The state of the network (a binary tuple).
Yields:
SystemIrreducibilityAnalysis: A |SIA| for each |Subsystem| of the
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15,873 | wmayner/pyphi | pyphi/compute/network.py | major_complex | def major_complex(network, state):
"""Return the major complex of the network.
Args:
network (Network): The |Network| of interest.
state (tuple[int]): The state of the network (a binary tuple).
Returns:
SystemIrreducibilityAnalysis: The |SIA| for the |Subsystem| with
maxima... | python | def major_complex(network, state):
"""Return the major complex of the network.
Args:
network (Network): The |Network| of interest.
state (tuple[int]): The state of the network (a binary tuple).
Returns:
SystemIrreducibilityAnalysis: The |SIA| for the |Subsystem| with
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15,874 | wmayner/pyphi | pyphi/compute/network.py | condensed | def condensed(network, state):
"""Return a list of maximal non-overlapping complexes.
Args:
network (Network): The |Network| of interest.
state (tuple[int]): The state of the network (a binary tuple).
Returns:
list[SystemIrreducibilityAnalysis]: A list of |SIA| for non-overlapping
... | python | def condensed(network, state):
"""Return a list of maximal non-overlapping complexes.
Args:
network (Network): The |Network| of interest.
state (tuple[int]): The state of the network (a binary tuple).
Returns:
list[SystemIrreducibilityAnalysis]: A list of |SIA| for non-overlapping
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15,875 | wmayner/pyphi | pyphi/examples.py | basic_network | def basic_network(cm=False):
"""A 3-node network of logic gates.
Diagram::
+~~~~~~~~+
+~~~~>| A |<~~~~+
| | (OR) +~~~+ |
| +~~~~~~~~+ | |
| | |
| v |
+~+~~~~~~+ +~~~~~+~+
... | python | def basic_network(cm=False):
"""A 3-node network of logic gates.
Diagram::
+~~~~~~~~+
+~~~~>| A |<~~~~+
| | (OR) +~~~+ |
| +~~~~~~~~+ | |
| | |
| v |
+~+~~~~~~+ +~~~~~+~+
... | [
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15,876 | wmayner/pyphi | pyphi/examples.py | basic_noisy_selfloop_network | def basic_noisy_selfloop_network():
"""Based on the basic_network, but with added selfloops and noisy edges.
Nodes perform deterministic functions of their inputs, but those inputs
may be flipped (i.e. what should be a 0 becomes a 1, and vice versa) with
probability epsilon (eps = 0.1 here).
Diagr... | python | def basic_noisy_selfloop_network():
"""Based on the basic_network, but with added selfloops and noisy edges.
Nodes perform deterministic functions of their inputs, but those inputs
may be flipped (i.e. what should be a 0 becomes a 1, and vice versa) with
probability epsilon (eps = 0.1 here).
Diagr... | [
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15,877 | wmayner/pyphi | pyphi/examples.py | residue_network | def residue_network():
"""The network for the residue example.
Current and previous state are all nodes OFF.
Diagram::
+~~~~~~~+ +~~~~~~~+
| A | | B |
+~~>| (AND) | | (AND) |<~~+
| +~~~~~~~+ +~~~~~~~+ |
... | python | def residue_network():
"""The network for the residue example.
Current and previous state are all nodes OFF.
Diagram::
+~~~~~~~+ +~~~~~~~+
| A | | B |
+~~>| (AND) | | (AND) |<~~+
| +~~~~~~~+ +~~~~~~~+ |
... | [
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15,878 | wmayner/pyphi | pyphi/examples.py | propagation_delay_network | def propagation_delay_network():
"""A version of the primary example from the IIT 3.0 paper with
deterministic COPY gates on each connection. These copy gates essentially
function as propagation delays on the signal between OR, AND and XOR gates
from the original system.
The current and previous st... | python | def propagation_delay_network():
"""A version of the primary example from the IIT 3.0 paper with
deterministic COPY gates on each connection. These copy gates essentially
function as propagation delays on the signal between OR, AND and XOR gates
from the original system.
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15,879 | wmayner/pyphi | pyphi/examples.py | macro_network | def macro_network():
"""A network of micro elements which has greater integrated information
after coarse graining to a macro scale.
"""
tpm = np.array([[0.3, 0.3, 0.3, 0.3],
[0.3, 0.3, 0.3, 0.3],
[0.3, 0.3, 0.3, 0.3],
[0.3, 0.3, 1.0, 1.0],
... | python | def macro_network():
"""A network of micro elements which has greater integrated information
after coarse graining to a macro scale.
"""
tpm = np.array([[0.3, 0.3, 0.3, 0.3],
[0.3, 0.3, 0.3, 0.3],
[0.3, 0.3, 0.3, 0.3],
[0.3, 0.3, 1.0, 1.0],
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15,880 | wmayner/pyphi | pyphi/examples.py | blackbox_network | def blackbox_network():
"""A micro-network to demonstrate blackboxing.
Diagram::
+----------+
+-------------------->+ A (COPY) + <---------------+
| +----------+ |
| +----------+ ... | python | def blackbox_network():
"""A micro-network to demonstrate blackboxing.
Diagram::
+----------+
+-------------------->+ A (COPY) + <---------------+
| +----------+ |
| +----------+ ... | [
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15,881 | wmayner/pyphi | pyphi/examples.py | actual_causation | def actual_causation():
"""The actual causation example network, consisting of an ``OR`` and
``AND`` gate with self-loops.
"""
tpm = np.array([
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 1, 0, 0],
[0, 0, 0, 1]
])
cm = np.array([
[1, 1],
[1, 1]
])
retu... | python | def actual_causation():
"""The actual causation example network, consisting of an ``OR`` and
``AND`` gate with self-loops.
"""
tpm = np.array([
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 1, 0, 0],
[0, 0, 0, 1]
])
cm = np.array([
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15,882 | wmayner/pyphi | pyphi/examples.py | prevention | def prevention():
"""The |Transition| for the prevention example from Actual Causation
Figure 5D.
"""
tpm = np.array([
[0.5, 0.5, 1],
[0.5, 0.5, 0],
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[0.5, 0.5, 1],
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[0.5, 0.5, 1],
[0.5, 0.5, 1]
... | python | def prevention():
"""The |Transition| for the prevention example from Actual Causation
Figure 5D.
"""
tpm = np.array([
[0.5, 0.5, 1],
[0.5, 0.5, 0],
[0.5, 0.5, 1],
[0.5, 0.5, 1],
[0.5, 0.5, 1],
[0.5, 0.5, 0],
[0.5, 0.5, 1],
[0.5, 0.5, 1]
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15,883 | wmayner/pyphi | benchmarks/benchmarks/subsystem.py | clear_subsystem_caches | def clear_subsystem_caches(subsys):
"""Clear subsystem caches"""
try:
# New-style caches
subsys._repertoire_cache.clear()
subsys._mice_cache.clear()
except TypeError:
try:
# Pre cache.clear() implementation
subsys._repertoire_cache.cache = {}
... | python | def clear_subsystem_caches(subsys):
"""Clear subsystem caches"""
try:
# New-style caches
subsys._repertoire_cache.clear()
subsys._mice_cache.clear()
except TypeError:
try:
# Pre cache.clear() implementation
subsys._repertoire_cache.cache = {}
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15,884 | wmayner/pyphi | pyphi/utils.py | all_states | def all_states(n, big_endian=False):
"""Return all binary states for a system.
Args:
n (int): The number of elements in the system.
big_endian (bool): Whether to return the states in big-endian order
instead of little-endian order.
Yields:
tuple[int]: The next state of ... | python | def all_states(n, big_endian=False):
"""Return all binary states for a system.
Args:
n (int): The number of elements in the system.
big_endian (bool): Whether to return the states in big-endian order
instead of little-endian order.
Yields:
tuple[int]: The next state of ... | [
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15,885 | wmayner/pyphi | pyphi/utils.py | np_hash | def np_hash(a):
"""Return a hash of a NumPy array."""
if a is None:
return hash(None)
# Ensure that hashes are equal whatever the ordering in memory (C or
# Fortran)
a = np.ascontiguousarray(a)
# Compute the digest and return a decimal int
return int(hashlib.sha1(a.view(a.dtype)).hex... | python | def np_hash(a):
"""Return a hash of a NumPy array."""
if a is None:
return hash(None)
# Ensure that hashes are equal whatever the ordering in memory (C or
# Fortran)
a = np.ascontiguousarray(a)
# Compute the digest and return a decimal int
return int(hashlib.sha1(a.view(a.dtype)).hex... | [
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15,886 | wmayner/pyphi | pyphi/utils.py | powerset | def powerset(iterable, nonempty=False, reverse=False):
"""Generate the power set of an iterable.
Args:
iterable (Iterable): The iterable from which to generate the power set.
Keyword Args:
nonempty (boolean): If True, don't include the empty set.
reverse (boolean): If True, reverse... | python | def powerset(iterable, nonempty=False, reverse=False):
"""Generate the power set of an iterable.
Args:
iterable (Iterable): The iterable from which to generate the power set.
Keyword Args:
nonempty (boolean): If True, don't include the empty set.
reverse (boolean): If True, reverse... | [
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15,887 | wmayner/pyphi | pyphi/utils.py | load_data | def load_data(directory, num):
"""Load numpy data from the data directory.
The files should stored in ``../data/<dir>`` and named
``0.npy, 1.npy, ... <num - 1>.npy``.
Returns:
list: A list of loaded data, such that ``list[i]`` contains the the
contents of ``i.npy``.
"""
root = ... | python | def load_data(directory, num):
"""Load numpy data from the data directory.
The files should stored in ``../data/<dir>`` and named
``0.npy, 1.npy, ... <num - 1>.npy``.
Returns:
list: A list of loaded data, such that ``list[i]`` contains the the
contents of ``i.npy``.
"""
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15,888 | wmayner/pyphi | pyphi/utils.py | time_annotated | def time_annotated(func, *args, **kwargs):
"""Annotate the decorated function or method with the total execution
time.
The result is annotated with a `time` attribute.
"""
start = time()
result = func(*args, **kwargs)
end = time()
result.time = round(end - start, config.PRECISION)
r... | python | def time_annotated(func, *args, **kwargs):
"""Annotate the decorated function or method with the total execution
time.
The result is annotated with a `time` attribute.
"""
start = time()
result = func(*args, **kwargs)
end = time()
result.time = round(end - start, config.PRECISION)
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15,889 | wmayner/pyphi | pyphi/models/mechanism.py | _null_ria | def _null_ria(direction, mechanism, purview, repertoire=None, phi=0.0):
"""The irreducibility analysis for a reducible mechanism."""
# TODO Use properties here to infer mechanism and purview from
# partition yet access them with .mechanism and .partition
return RepertoireIrreducibilityAnalysis(
... | python | def _null_ria(direction, mechanism, purview, repertoire=None, phi=0.0):
"""The irreducibility analysis for a reducible mechanism."""
# TODO Use properties here to infer mechanism and purview from
# partition yet access them with .mechanism and .partition
return RepertoireIrreducibilityAnalysis(
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15,890 | wmayner/pyphi | pyphi/models/mechanism.py | MaximallyIrreducibleCauseOrEffect.damaged_by_cut | def damaged_by_cut(self, subsystem):
"""Return ``True`` if this MICE is affected by the subsystem's cut.
The cut affects the MICE if it either splits the MICE's mechanism
or splits the connections between the purview and mechanism.
"""
return (subsystem.cut.splits_mechanism(self... | python | def damaged_by_cut(self, subsystem):
"""Return ``True`` if this MICE is affected by the subsystem's cut.
The cut affects the MICE if it either splits the MICE's mechanism
or splits the connections between the purview and mechanism.
"""
return (subsystem.cut.splits_mechanism(self... | [
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15,891 | wmayner/pyphi | pyphi/models/mechanism.py | Concept.eq_repertoires | def eq_repertoires(self, other):
"""Return whether this concept has the same repertoires as another.
.. warning::
This only checks if the cause and effect repertoires are equal as
arrays; mechanisms, purviews, or even the nodes that the mechanism
and purview indices ... | python | def eq_repertoires(self, other):
"""Return whether this concept has the same repertoires as another.
.. warning::
This only checks if the cause and effect repertoires are equal as
arrays; mechanisms, purviews, or even the nodes that the mechanism
and purview indices ... | [
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15,892 | wmayner/pyphi | pyphi/models/mechanism.py | Concept.emd_eq | def emd_eq(self, other):
"""Return whether this concept is equal to another in the context of
an EMD calculation.
"""
return (self.phi == other.phi and
self.mechanism == other.mechanism and
self.eq_repertoires(other)) | python | def emd_eq(self, other):
"""Return whether this concept is equal to another in the context of
an EMD calculation.
"""
return (self.phi == other.phi and
self.mechanism == other.mechanism and
self.eq_repertoires(other)) | [
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15,893 | wmayner/pyphi | pyphi/actual.py | directed_account | def directed_account(transition, direction, mechanisms=False, purviews=False,
allow_neg=False):
"""Return the set of all |CausalLinks| of the specified direction."""
if mechanisms is False:
mechanisms = utils.powerset(transition.mechanism_indices(direction),
... | python | def directed_account(transition, direction, mechanisms=False, purviews=False,
allow_neg=False):
"""Return the set of all |CausalLinks| of the specified direction."""
if mechanisms is False:
mechanisms = utils.powerset(transition.mechanism_indices(direction),
... | [
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15,894 | wmayner/pyphi | pyphi/actual.py | account | def account(transition, direction=Direction.BIDIRECTIONAL):
"""Return the set of all causal links for a |Transition|.
Args:
transition (Transition): The transition of interest.
Keyword Args:
direction (Direction): By default the account contains actual causes
and actual effects... | python | def account(transition, direction=Direction.BIDIRECTIONAL):
"""Return the set of all causal links for a |Transition|.
Args:
transition (Transition): The transition of interest.
Keyword Args:
direction (Direction): By default the account contains actual causes
and actual effects... | [
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15,895 | wmayner/pyphi | pyphi/actual.py | _evaluate_cut | def _evaluate_cut(transition, cut, unpartitioned_account,
direction=Direction.BIDIRECTIONAL):
"""Find the |AcSystemIrreducibilityAnalysis| for a given cut."""
cut_transition = transition.apply_cut(cut)
partitioned_account = account(cut_transition, direction)
log.debug("Finished evalua... | python | def _evaluate_cut(transition, cut, unpartitioned_account,
direction=Direction.BIDIRECTIONAL):
"""Find the |AcSystemIrreducibilityAnalysis| for a given cut."""
cut_transition = transition.apply_cut(cut)
partitioned_account = account(cut_transition, direction)
log.debug("Finished evalua... | [
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15,896 | wmayner/pyphi | pyphi/actual.py | _get_cuts | def _get_cuts(transition, direction):
"""A list of possible cuts to a transition."""
n = transition.network.size
if direction is Direction.BIDIRECTIONAL:
yielded = set()
for cut in chain(_get_cuts(transition, Direction.CAUSE),
_get_cuts(transition, Direction.EFFECT)... | python | def _get_cuts(transition, direction):
"""A list of possible cuts to a transition."""
n = transition.network.size
if direction is Direction.BIDIRECTIONAL:
yielded = set()
for cut in chain(_get_cuts(transition, Direction.CAUSE),
_get_cuts(transition, Direction.EFFECT)... | [
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15,897 | wmayner/pyphi | pyphi/actual.py | sia | def sia(transition, direction=Direction.BIDIRECTIONAL):
"""Return the minimal information partition of a transition in a specific
direction.
Args:
transition (Transition): The candidate system.
Returns:
AcSystemIrreducibilityAnalysis: A nested structure containing all the
data ... | python | def sia(transition, direction=Direction.BIDIRECTIONAL):
"""Return the minimal information partition of a transition in a specific
direction.
Args:
transition (Transition): The candidate system.
Returns:
AcSystemIrreducibilityAnalysis: A nested structure containing all the
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15,898 | wmayner/pyphi | pyphi/actual.py | nexus | def nexus(network, before_state, after_state,
direction=Direction.BIDIRECTIONAL):
"""Return a tuple of all irreducible nexus of the network."""
validate.is_network(network)
sias = (sia(transition, direction) for transition in
transitions(network, before_state, after_state))
return... | python | def nexus(network, before_state, after_state,
direction=Direction.BIDIRECTIONAL):
"""Return a tuple of all irreducible nexus of the network."""
validate.is_network(network)
sias = (sia(transition, direction) for transition in
transitions(network, before_state, after_state))
return... | [
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15,899 | wmayner/pyphi | pyphi/actual.py | causal_nexus | def causal_nexus(network, before_state, after_state,
direction=Direction.BIDIRECTIONAL):
"""Return the causal nexus of the network."""
validate.is_network(network)
log.info("Calculating causal nexus...")
result = nexus(network, before_state, after_state, direction)
if result:
... | python | def causal_nexus(network, before_state, after_state,
direction=Direction.BIDIRECTIONAL):
"""Return the causal nexus of the network."""
validate.is_network(network)
log.info("Calculating causal nexus...")
result = nexus(network, before_state, after_state, direction)
if result:
... | [
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